Inquiry Post 2: Testing AI Tools for Resume Improvement
Introduction
In my first inquiry post, I introduced my plan to explore AI-assisted resume development and digital portfolios as part of my learning process in this course. As a Computer Science student preparing for co-op opportunities and future roles in the technology industry, I wanted to investigate how artificial intelligence tools can help improve resumes while still maintaining accuracy and authenticity.
For this stage of my inquiry, I conducted a small experiment using several AI tools to revise my existing resume. The purpose of this experiment was to observe how different AI systems interpret the same request and how they modify resume content. Specifically, I wanted to examine how AI tools improve clarity, emphasize technical skills, and structure professional experience.
In addition to testing the tools myself, I also compared my findings with the results from my group member Aiden, who experimented with generating resume content based on project descriptions rather than modifying an existing resume.
Experiment Setup
To begin the experiment, I used my current resume as a baseline. I then asked several AI tools to revise the resume using the same prompt. The tools I tested included:
- ChatGPT
- Gemini
- Microsoft Copilot
Using the same prompt allowed me to compare how each system interpreted the request and what types of revisions it produced.
The prompt used for the experiment was:
I am a Computer Science student applying for IT internships.
Please improve the bullet points in my resume while keeping the information accurate.Focus on:
- clarity
- measurable impact
- strong action verbs
- relevant technical keywords.
Do not invent experience or exaggerate responsibilities.
The goal was to evaluate how each system revised the resume while keeping the underlying information truthful.
Resume Revision Examples
For privacy reasons, I did not include my full resume in this post. Instead, I present several shortened examples of bullet points that demonstrate how each AI system revised the content. These examples show how different tools improved clarity, emphasized technical skills, and framed work experience.
Example 1: Technical Support Tasks
Original:
Resolved 500+ technical support tickets for employees involving hardware, software, and access issues.
ChatGPT Revision:
Resolved 500+ technical support tickets through ServiceNow, troubleshooting device, application, and access issues for employees.
Gemini Revision:
Resolved technical support requests while maintaining service-level standards (SLA) for hardware, software, and access management across multiple branches.
Observation:
Both AI tools improved the clarity and professionalism of the bullet point. ChatGPT emphasized measurable impact by highlighting the number of tickets resolved, while Gemini introduced industry terminology such as “SLA standards,” which reflects language commonly used in IT support environments.
Example 2: Device Deployment
Original:
Imaged, tested, and deployed Window devices; configured hybrid Azure AD and on-prem Active Directory joins.
ChatGPT Revision:
Imaged, configured, and deployed Windows devices completing hybrid Azure AD and on-prem Active Directory domain joins.
Gemini Revision:
Streamlined device lifecycle management by imaging, testing, and deploying Windows workstations via hybrid Azure AD and on-prem Active Directory joins.
Observation:
In this case, ChatGPT focused on improving sentence clarity and emphasizing the deployment process. Gemini, however, reframed the task using more strategic terminology such as “device lifecycle management,” which presents the work in a broader enterprise IT context.
These examples illustrate how different AI tools approach resume improvement. While both systems strengthened the wording and emphasized technical details, they differed in tone and framing. ChatGPT focused on clarity and measurable impact, while Gemini often introduced broader enterprise terminology and strategic language.
Tool Comparison
The table below summarizes the main differences I observed between the tools.
| Category | ChatGPT | Gemini | Copilot |
| Clarity | Improved readability and structure | Clear but sometimes more unnecessary | Similar to Gemini |
| Technical Keywords | Highlighted existing tools | Added industry terminology | Similar emphasis on enterprise language |
| Authenticity | Stayed close to original experience | Sometimes reframed tasks more strategically | Similar to Gemini |
| Tone | Natural and professional | Corporate and highly technical | Professional but formal |
| Risk | Slightly generic phrasing | Possible exaggeration of responsibilities | Similar to Gemini |
This comparison helped illustrate that while all tools attempted to improve the resume, they prioritized different aspects of professional writing.
ChatGPT Results
When testing ChatGPT, I found that the system primarily focused on refining the existing information while improving clarity and structure. Many of the revisions involved strengthening action verbs and emphasizing measurable impact. For example, ChatGPT highlighted that I resolved “500+ technical support tickets,” which clearly communicates the scale of my work experience.
ChatGPT also emphasized the technologies used in my role, such as ServiceNow, Azure Active Directory, Windows Server, and PDQ Deploy. These tools were already present in the original resume, but the AI reorganized the wording to make them more prominent.
Overall, the revisions felt natural and closely aligned with my actual experience. ChatGPT improved the readability of the resume without dramatically changing the meaning of my work responsibilities. Because of this, the tool felt more like a collaborative writing assistant rather than something that was replacing my own voice.
Gemini Results
Gemini produced noticeably different revisions compared to ChatGPT. Instead of simply refining the existing wording, Gemini often reframed tasks using broader or more strategic terminology.
For example, routine technical support tasks were described using phrases such as “device lifecycle management,” “SLA standards,” and “Tier 2 escalations.” In some cases, everyday responsibilities were presented in ways that made them sound more strategic or organizationally significant.
One example described troubleshooting meeting room technology as supporting “business continuity in high-stakes meeting environments.” While this phrasing highlights the importance of the task, it also illustrates how AI systems can sometimes elevate or exaggerate routine responsibilities if the output is not carefully reviewed.
Gemini also provided short explanations describing why it made certain changes, which was helpful in understanding how the system interpreted the prompt.
Microsoft Copilot Results
Microsoft Copilot produced results that were largely similar to Gemini. The tool emphasized technical terminology and enterprise-style language while restructuring sentences to highlight operational impact.
While these revisions made the resume sound more formal and technically sophisticated, the tone occasionally felt more corporate than the original resume. As with Gemini, the outputs required careful review to ensure that the wording accurately reflected the scope of my responsibilities.
Group Observations: Aiden’s Experiment
While my experiment focused on improving an existing resume, my group member Aiden explored a different approach by generating resumes from project descriptions.
Aiden also tested ChatGPT, Gemini, and Microsoft Copilot. In his experiment, ChatGPT again produced the most balanced results and was generally preferred. The project descriptions generated by ChatGPT were clearer and more descriptive, making it easier to understand the technical purpose of the projects.
Gemini focused heavily on technical terminology and keywords, but some outputs felt less descriptive and less analytical about the actual project goals. Copilot produced results similar to Gemini in terms of tone and structure.
- Aiden also experimented with generating resume templates using LaTeX in Overleaf. In this case, Gemini performed well when producing LaTeX formatting, although the outputs from all three tools were relatively similar.
Key Observations
One surprising observation from this experiment was how differently AI systems approached the same prompt. I initially expected the outputs to be very similar, but each system prioritized different aspects of resume writing.
ChatGPT focused on improving clarity and readability, while Gemini emphasized industry terminology and strategic language. This difference suggests that AI tools may be optimized for different writing styles and professional contexts.
Reflection
This experiment helped me understand both the strengths and limitations of AI-assisted resume development. On one hand, AI tools can significantly improve the clarity and professionalism of resume content by strengthening action verbs and highlighting technical skills. On the other hand, AI systems sometimes introduce language that makes tasks sound more strategic or impactful than they originally were.
These observations highlight an important aspect of digital literacy. While AI tools can be extremely helpful for improving writing and structure, users must critically evaluate the outputs rather than accepting them automatically. Human judgment remains essential to ensure that resumes accurately represent real experience.
Connection to Digital Literacy
This experiment also connects to broader discussions about AI in hiring systems and digital literacy. As organizations increasingly rely on automated tools to screen resumes, applicants may feel pressure to optimize their resumes using AI-generated keywords.
However, relying too heavily on AI-generated language may produce resumes that sound generic or exaggerated. Learning how to use these tools responsibly is therefore an important skill for students entering the workforce.
Next Steps
In the next stage of my inquiry, I plan to explore how AI tools can assist with generating and formatting resumes using LaTeX. This will involve experimenting with tools such as Overleaf and evaluating whether AI can simplify the process of creating professionally formatted technical resumes.
This next step will focus more on skill development while continuing to reflect on how AI tools influence professional communication and digital identity.